A new hybrid approach for crude oil price forecasting: Evidence from multi-scale data

TL;DR

This paper introduces a hybrid multi-scale data approach combining GSVI and economic data with advanced machine learning techniques to improve monthly crude oil price forecasting accuracy.

Contribution

It proposes a novel hybrid divide-and-conquer method integrating K-means, KPCA, and KELM for enhanced crude oil price prediction using multi-source data.

Findings

GSVI data outperforms economic data in level forecasting

Hybrid data achieves best accuracy in both level and directional forecasts

Divide and conquer strategy improves forecasting performance

Abstract

Faced with the growing research towards crude oil price fluctuations influential factors following the accelerated development of Internet technology, accessible data such as Google search volume index are increasingly quantified and incorporated into forecasting approaches. In this paper, we apply multi-scale data that including both GSVI data and traditional economic data related to crude oil price as independent variables and propose a new hybrid approach for monthly crude oil price forecasting. This hybrid approach, based on divide and conquer strategy, consists of K-means method, kernel principal component analysis and kernel extreme learning machine , where K-means method is adopted to divide input data into certain clusters, KPCA is applied to reduce dimension, and KELM is employed for final crude oil price forecasting. The empirical result can be analyzed from data and method levels. At the data level, GSVI data perform better than economic data in level forecasting accuracy but with opposite performance in directional forecasting accuracy because of Herd Behavior, while hybrid data combined their advantages and obtain best forecasting performance in both level and directional accuracy. At the method level, the approaches with K-means perform better than those without K-means, which demonstrates that divide and conquer strategy can effectively improve the forecasting performance.